Method and system for focus-adaptive reconstruction of spine images

ABSTRACT

A method for aligning a pair of digital images includes providing a pair of digital images, wherein each said image comprises a plurality of intensities corresponding to a domain of points in a D-dimensional space, and the pair of images present adjacent views of a same object of interest. A weighting function is applied to each image of the pair of images, wherein the weighting function is centered on the object of interest, the weighting function has a maximum value on the object of interest, and the value of the weighting function decreases with increasing distance from the object of interest. The pair of images is aligned by correlated the weighted intensities on one image with those in the other image.

CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS

This application claims priority from “Focus-adaptive Method for MRSpine Composer”, U.S. Provisional Application No. 60/516,793 of Zhang,et al., filed Nov. 3, 2003, the contents of which are incorporatedherein by reference.

TECHNICAL FIELD

This invention is directed to methods of constructing a composite imagevolume from several ordered constituent volumes of medical images.

DISCUSSION OF THE RELATED ART

The diagnostically superior information available from data acquiredfrom current imaging systems enables the detection of potential problemsat earlier and more treatable stages. Given the vast quantity ofdetailed data acquirable from imaging systems, various algorithms mustbe developed to efficiently and accurately process image data. With theaid of computers, advances in image processing are generally performedon digital or digitized images.

Digital images are created from an array of numerical valuesrepresenting a property (such as a grey scale value or magnetic fieldstrength) associable with an anatomical location points referenced by aparticular array location. The set of anatomical location pointscomprises the domain of the image. In 2-D digital images, or slicesections, the discrete array locations are termed pixels.Three-dimensional digital images can be constructed from stacked slicesections through various construction techniques known in the art. The3-D images are made up of discrete volume elements, also referred to asvoxels, composed of pixels from the 2-D images. The pixel or voxelproperties can be processed to ascertain various properties about theanatomy of a patient associated with such pixels or voxels.

In many diagnostic imaging situations, the target object to be imaged ismuch larger than the field of view of the imaging device. Even when itis possible to increase the field of view to cover the whole targetobject, the resulting image has insufficient resolution or possiblegeometric distortions in the off-center areas. It may also not bepossible to cover the entire target object in one image because of thegeometry or topology of the target object. It is nevertheless useful topresent the entire target object in a single image to the human for thepurpose of diagnosis. Moreover, it is important to compose the imagewith certain precision requirement for quantitative measurement in manyclinical applications. For example, many musculoskeletal disorders suchas scoliosis require the examination of the spine as a whole so that itsgeometry can be seen or measured. Due to the size of spine, it iscurrently not possible to acquire a single MR image of the entire spinewithout lowering resolution, adding significant distortions, ordeteriorating contrast. In present radiological practice, partial,overlapping constituent images are taken at several stations along thespine, starting from the back of the head down to the pelvis. Theoverlaps between the images can vary. Seam positions between individualvolumes need to be accurate for the composite volume to look reasonable.This is very important in clinical diagnosis. A composite volume withpoor quality could cause difficulties in physician's diagnosticdecision-making process. The ability to view the entire object on asingle image facilitates convenient and accurate diagnostic examinationand measurement.

The process of forming a compound image from overlapped, individualimages is referred to herein as image composing. Prior systems for imagecomposing include U.S. Pat. No. 6,101,238, titled “System for generatinga compound x-ray image for diagnosis”, issued on Aug. 8, 2000, and U.S.Pat. No. 6,757,418, titled “Method and system for automatic computedradiography (CR) image composition by white band detection andconsistency rechecking”, issued on Jun. 29, 2004. These patents disclosealignment of images using cross-correlation techniques and horizontaland vertical translations.

Magnetic resonance composing refers to the automatic constructing of onecomposite 3D volume from several constituent ordered 3D volumes of MRimages. When imaging the spine, the scans are performed so that there isan overlap region in each image. Thus, when considering a pair ofimages, one of an upper portion and one of a lower portion of a spine,the lower part of the upper region should overlap with the upper part ofthe lower region. The images are composed by aligning voxels of theupper overlap region with corresponding voxels of the lower overlapregion. One seeks to determine a cut-line in the overlap region, whereone image ends and the next image begins. The goodness of the alignmentcan be determined by correlating one overlap region with the other. Byshifting the alignment parameters of each pairs of volumes in a 3-Dsearch range (horizontal, vertical and depth) and computing thecorrelation of all the possible alignments within the search range forthe overlap parts of the volumes, the highest correlation can beautomatically found and the corresponding alignment parameters returnedas the best match for the pair of volumes.

In theory, this scheme should work well for ideally overlapped volumes.The best match always occurs exactly where the overlap parts of eachpair of volumes completely match each other, which is corresponding tothe highest correlation among all possible alignments. However inpractice, when working with real data, this is not always true. If theoriginal images are distorted in some way, it may not be possible tomatch all parts of the volumes simultaneously. In this case, theprocedure can still return the highest correlation as ‘the best match’,which may not necessarily be the best match defined by a human.

SUMMARY OF THE INVENTION

In one aspect of the invention, there is provided a method for aligninga pair of digital images. The method includes providing a pair ofdigital images, wherein each said image comprises a plurality ofintensities corresponding to a domain of points in a D-dimensionalspace, and the pair of images present adjacent views of a same object ofinterest, applying a weighting function to each image of the pair ofimages, wherein the weighting function is centered on the object ofinterest, the weighting function has a maximum value on the object ofinterest, and the value of the weighting function decreases withincreasing distance from the object of interest, and aligning the pairof images by correlated the weighted intensities on one image with thosein the other image.

In a further aspect of the invention, each image of the pair of imageshas an overlap region, and wherein the step of aligning is performed bycorrelating the weighted intensities in the overlap region of one imagewith the weighted intensities in the overlap region of the other image.

In a further aspect of the invention, the two images are joined into acomposed image at a cutline in the overlap regions of the two images.

In a further aspect of the invention, the weighting function is appliedto an image in the pair of images by multiplying the intensity of apoint in the image by the value of the weighting function.

In a further aspect of the invention, the object of interest in eachimage is the spine.

In a further aspect of the invention, the object of interest in eachimage is an arm.

In a further aspect of the invention, the object of interest in eachimage is a leg.

In a further aspect of the invention, the images are magnetic resonanceimages.

In a further aspect of the invention, the weighting function is aGaussian of the formP(x)=exp(−x ²/2σ²)wherein the x-axis is along the image width direction, with the imagecenter as x=0, where P(x)=1.0, and σ=width/6.

In a further aspect of the invention, the weighting function is of theformP(x)=σ^(2n)/(σ^(2n) +x ^(2n)),wherein the x-axis is along the image width direction, with the imagecenter line defined as x=0, where P(x)=1.0, n can be any positiveinteger and σ=width/6.

In a further aspect of the invention, the weighting function is of theform

${{P(x)} = {{- \left( \frac{2}{w} \right)^{2n}}\left( {x^{2} - \left( \frac{w}{2} \right)^{2}} \right)^{n}}},$wherein the x-axis is along the image width direction, with the imagecenter line defined as x=0, where P(x)=1.0, n is any positive integerand w is the image width.

In a further aspect of the invention, the weighting function is of theformP(x)={−2x/w+1:0≦x≦w/2;2x/+1:−w/2≦x<0},wherein the x-axis is along the image width direction, with the imagecenter line defined as x=0, where P(x)=1.0, and w is the image width.

In another aspect of the invention, there is provided a program storagedevice readable by a computer, tangibly embodying a program ofinstructions executable by the computer to perform the method steps foraligning a pair of digital images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary Gaussian weighing curve.

FIG. 2 depicts the result of a dataset with the original composingmethod.

FIG. 3 depicts the result using a focus-adaptive method on the samedataset as depicted in FIG. 2.

FIG. 4 depicts the result for the c-spine and t-spine, using theoriginal composing method and the dataset of FIGS. 2. and 3.

FIG. 5 depicts the result for the c-spine and t-spine, using afocus-adaptive composing method and the dataset of FIGS. 2. and 3.

FIG. 6 depicts the result using another dataset composed with theoriginal composing method.

FIG. 7 depicts the result using the dataset as in FIG. 6, but composedwith a focus-adaptive method.

FIG. 8 illustrates the two-slice problem composing result with theoriginal composing method.

FIG. 9 illustrates the two-slice problem composing result with afocus-adaptive composing method.

FIG. 10 depicts a flow chart of a preferred method of the invention.

FIG. 11 depicts an exemplary computer system for implementing apreferred embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Illustrative embodiments of the invention are described below. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will of course be appreciated thatin the development of any such actual embodiment, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which will vary from one implementation toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming, but would nevertheless be a routineundertaking for those of ordinary skill in the art having the benefit ofthis disclosure.

The methods and systems disclosed herein can be adapted toreconstructing a composite volume from several constituent orderedvolumes of organs or anatomical regions including, without limitation,the spine, and limbs such as arms and legs. The software application andalgorithm disclosed herein can employ 2-D and 3-D renderings and imagesof an organ or anatomical system. For illustrative purposes, a spinalsystem is described. However, it should be understood that the methodcan be applied to any of a variety of other applications, such as thearms and legs, as is known to those skilled in the art.

In the reconstruction of spinal images, computing the correlation of allthe possible alignments for the overlap regions of the volumes sometimesprovides poor alignment results, as the spine is not always aligned bythe automatic alignment parameters computed by the composing algorithm.It has been found that poor alignment can be caused by inconsistency ofthe volume pair overlaps.

The 3-D correlation method works well with ideal images, whereoverlapping parts of each pair of volumes are consistent. Here, thecorrelation method provides perfect alignment results for ideal inputimages. However for inconsistent images, there is no perfect match. Onepossible cause for the inconsistency is the process of distortioncorrection. Since MR machines can sometimes cause image distortions, theindividual original MR image volumes need to be distortion-correctedbefore being sent as the inputs for the composing process. However, thedistortion corrected images might not be consistent with real data. Ifinput images are warped to some degree, it is not possible to find aperfect match between image pairs even by manual adjustment. Forexample, in the case of a sagittal dataset, there is frequently a brightstripe along the patient's back due to the surface coil used in the dataacquisition. If the sagittal image happens to be distorted, it might bepossible to match either the spine area, which is usually near thecenter of the image, or the stripe area, which is at the edge of theimage, but not both simultaneously. Correlation computation, by itsnature, favors matching large homogeneous areas over other areas.Therefore, the best alignment computed automatically by a pure 3-Dcorrelation algorithm may tend to favor positions where the patient'sback match. In this case, the highest correlation of a back image matchis misleading, because the composed image is misaligned for the spinalarea, which is the image portion of interest.

The methods presented herein, referred to as focus adapted methods,mimic the natural process of human manual adjustments for alignmentparameters. When performing manual adjustments of spinal images, peopletend to focus more on the part of the image where the spine is located,and not on the peripheral regions of the images, since the spinal areais more important for clinical diagnosis. In a focus adaptive method,which places more weight on the spinal area, the alignment of the spineautomatically computed by the correlation process can be improved.Assuming that the spinal area is in the center of the image, pixels onthe center line are given an image a weight of 1.0, and pixelintensities are gradually suppressed toward the left and right sides ofthe image. Note that the weight is applied only while computingalignment parameters, thus the image itself is not modified. Once thealignment parameters are obtained, the original images are composedaccording to these parameters.

Referring now to the flowchart depicted in FIG. 10, the weight functionis applied at steps 1001, 1002 to each image of a pair of images to bealigned. The application of the weighting function can be performed bymultiplying the intensity at each point in each image by an appropriatevalue of the weighting function. In one embodiment, the weighing curvechosen is a Gaussian distribution, depicted in FIG. 1. An exemplaryGaussian distribution P(x) can be defined as P(x)=exp(−x²/2σ²). TheX-axis can be taken to be along the image width direction, with theimage center line defined as x=0, where P(x)=1.0, representing nosuppression on the center line. To determine the shape of the Gaussiancurve, i.e. how sharp the suppression should be, one needs to choose avalue for the standard deviation, σ. In this embodiment, the standarddeviation is chosen to be +3σ at the right edge, and −3σ at the leftedge. Therefore, the total image width is 6σ, i.e. σ=width/6. This valueof σ is for example only, and other values of σ are possible and withinthe scope of the invention. The weight can be computed at every locationalong the image width direction and it can be applied to the image pixelvalues before computing the correlation value. After application of theweighting function to each of the two images, the images can be alignedas before by computing the correlation value at step 1003 for theweighted image values in the overlap regions of each image. The alignedimages can then be joined at step 1004 at a cutline in the overlapregions. This method of applying a weight function to the images beforecorrelating the overlap regions can be performed 1005 for eachsuccessive pair of images to be joined into the final composed image. Byusing this focus-adaptive method in spine images, more reasonablecomposing results can be obtained when the volume pairs are notcompletely consistent.

Note that other weighting distributions can be used for the focusadaptive method. In another embodiment, the weight function is of theform P(x)=σ^(2n)/(σ^(2n)+x^(2n)), where n can be any positive integerand σ can be chosen as before. In another embodiment, the weightfunction is of the form

${{P(x)} = {{- \left( \frac{2}{w} \right)^{2n}}\left( {x^{2} - \left( \frac{w}{2} \right)^{2}} \right)^{n}}},$where again n is any positive integer and w is the image width. Inanother embodiment, the weight function is of the formP(x)={−2x/w+1:0≦x≦w/2 /2x/w+1:−w/2≦x<0}, with w being the image width.These weight functions are exemplary, and any weight function that has amaximum value at a line of reflection in the image, and whose valuedecreases monotonically with increasing distance from the line ofreflection, is within the scope of the invention. Although the examplefunctions presented here are symmetric with respect to the reflectionaxis, this is not an absolute requirement, as long as the weightingdecreases with increasing distance from the reflection axis.

Test results show the performance improvement by using thefocus-adaptive methods of the invention. For those datasets that hadsatisfactory performance with the original composing algorithm, usingfocus-adaptive method will not affect the original alignment results.

A first example uses a dataset in which the images weredistortion-corrected, but where the distortion correction parameterswere not correctly chosen. This caused the images to be stronglydistorted. This extreme case is used to show how the focus-adaptivemethod can successfully overcome image distortion and still producereasonable and robust alignment results. FIG. 2 shows the alignmentresult with the original method. In clear-cut mode, the composingresults are poor, with the spine not at all aligned. FIG. 3 depicts theresult obtained on the same dataset by applying the focus-adaptivemethods disclosed herein. As can be seen, the composed image in FIG. 3is of much better quality.

The differences between the original method and the focus-adaptivemethod can be further explored by comparing FIGS. 4 and 5. These imagesrepresent results for the cervical spine (c-spine) and the thoracicspine (t-spine) in the previous dataset. The image depicted in FIG. 4was obtained using the original method. It can be seen from the cut-lineposition, that although the spine is poorly aligned, the bright area atthe patient's back is well aligned. Due to the inconsistency caused byimage distortion, it is not possible to match both the spine and thebright stripe area at the back. As discussed above, the correlationcomputation favors matching the large homogeneous areas, and thus thisalignment has the highest correlation among all possible alignmentcombinations in this case. The image depicted in FIG. 5 shows theresults obtained using the focus-adaptive method. Because thefocus-adaptive method suppressed the bright areas at the edge of theimage by putting less weight on it and focused more on the center areawhere spine is located, the composed result shows a good alignment alongthe spine, but not at the edge of the image. If the original inputimages are strongly distorted, the result in FIG. 5 is preferred overthat in FIG. 4, because the spine is of more significance to thediagnosis.

FIGS. 6 and 7 present results using another data set. The originalmethod provides the result of a vertical shift as being 378 pixels (FIG.6). This alignment needs a manual adjustment of 369 pixels, for adifference between automatic and manual adjustments of 9 pixels.However, by applying the focus-adaptive method, the ideal alignmentresult is obtained, as shown in FIG. 7.

Another example uses a test dataset with only two slices in each volume.The most reasonable composing result obtained by manual adjustmentshould not include incomplete slices, i.e. the optimal depth shiftshould be 0. However, the original method gives the result shown in FIG.8, which has incomplete slices, with a depth shift of −1 pixels betweenthe c-spine and the t-spine. The focus adaptive method solved theincomplete slice problem by focusing on the spine area, as shown in FIG.9. Note that the depth shift is 0 pixels between the c-spine and thet-spine in FIG. 9.

It is to be understood that the present invention can be implemented invarious forms of hardware, software, firmware, special purposeprocesses, or a combination thereof. In one embodiment, the presentinvention can be implemented in software as an application programtangible embodied on a computer readable program storage device. Theapplication program can be uploaded to, and executed by, a machinecomprising any suitable architecture.

Referring now to FIG. 11, according to an embodiment of the presentinvention, a computer system 1101 for implementing the present inventioncan comprise, inter alia, a central processing unit (CPU) 1102, a memory1103 and an input/output (I/O) interface 1104. The computer system 1101is generally coupled through the I/O interface 1104 to a display 1105and various input devices 1106 such as a mouse and a keyboard. Thesupport circuits can include circuits such as cache, power supplies,clock circuits, and a communication bus. The memory 1103 can includerandom access memory (RAM), read only memory (ROM), disk drive, tapedrive, etc., or a combinations thereof. The present invention can beimplemented as a routine 1107 that is stored in memory 1103 and executedby the CPU 1102 to process the signal from the signal source 1108. Assuch, the computer system 1101 is a general purpose computer system thatbecomes a specific purpose computer system when executing the routine1107 of the present invention.

The computer system 1101 also includes an operating system and microinstruction code. The various processes and functions described hereincan either be part of the micro instruction code or part of theapplication program (or combination thereof) which is executed via theoperating system. In addition, various other peripheral devices can beconnected to the computer platform such as an additional data storagedevice and a printing device.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which the present invention is programmed. Given the teachingsof the present invention provided herein, one of ordinary skill in therelated art will be able to contemplate these and similarimplementations or configurations of the present invention.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown, other than as describedin the claims below. It is therefore evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

1. A method for aligning a pair of digital images, said methodcomprising the steps of: providing a pair of digital images, whereineach said image comprises a plurality of intensities corresponding to adomain of points in a D-dimensional space, and the pair of imagespresent adjacent views of a same object of interest; applying aweighting function to each image of the pair of images, wherein theweighting function is centered on the object of interest, the weightingfunction has a maximum value on the object of interest, and the value ofthe weighting function decreases with increasing distance from theobject of interest; and aligning the pair of images by correlated theweighted intensities on one image with those in the other image.
 2. Themethod of claim 1, wherein each image of the pair of images has anoverlap region, and wherein the step of aligning is performed bycorrelating the weighted intensities in the overlap region of one imagewith the weighted intensities in the overlap region of the other image.3. The method of claim 2, wherein the two images are joined into acomposed image at a cutline in the overlap regions of the two images. 4.The method of claim 1, wherein the weighting function is applied to animage in the pair of images by multiplying the intensity of a point inthe image by the value of the weighting function.
 5. The method of claim1, wherein the object of interest in each image is the spine.
 6. Themethod of claim 1, wherein the object of interest in each image is anarm.
 7. The method of claim 1, wherein the object of interest in eachimage is a leg.
 8. The method of claim 1, wherein the images aremagnetic resonance images.
 9. The method of claim 1, wherein theweighting function is a Gaussian of the formP(x)=exp(−^(x) ² /2σ²) wherein the x-axis is along the image widthdirection, with the image center line defined as x=0, where P(x)=1.0,and σ=width/6.
 10. The method of claim 1, wherein the weighting functionis of the formP(x)=σ^(2n)/(σ^(2n) +x ^(2n)), wherein the x-axis is along the imagewidth direction, with the image center line defined as x=0, whereP(x)=1.0, n can be any positive integer and σ=width/6.
 11. The method ofclaim 1, wherein the weighting function is of the form${{P(x)} = {{- \left( \frac{2}{w} \right)^{2n}}\left( {x^{2} - \left( \frac{w}{2} \right)^{2}} \right)^{n}}},$wherein the x-axis is along the image width direction, with the imagecenter line defined as x=0, where P(x)=1.0, n is any positive integerand w is the image width.
 12. The method of claim 1, wherein theweighting function is of the formP(x)={−2x/w+1:0≦x≦w/2; 2x/w+1:−w/2≦x<0}, wherein the x-axis is along theimage width direction, with the image center line defined as x=0, whereP(x)=1.0, and w is the image width.
 13. A program storage devicereadable by a computer, tangibly embodying a program of instructionsexecutable by the computer to perform the method steps for aligning apair of digital images, said method comprising the steps of: providing apair of digital images, wherein each said image comprises a plurality ofintensities corresponding to a domain of points in a D-dimensionalspace, and the pair of images present adjacent views of a same object ofinterest; applying a weighting function to each image of the pair ofimages, wherein the weighting function is centered on the object ofinterest, the weighting function has a maximum value on the object ofinterest, and the value of the weighting function decreases withincreasing distance from the object of interest; and aligning the pairof images by correlated the weighted intensities on one image with thosein the other image.
 14. The computer readable program storage device ofclaim 13, wherein each image of the pair of images has an overlapregion, and wherein the step of aligning is performed by correlating theweighted intensities in the overlap region of one image with theweighted intensities in the overlap region of the other image.
 15. Thecomputer readable program storage device of claim 14, wherein the twoimages are joined into a composed image at a cutline in the overlapregions of the two images.
 16. The computer readable program storagedevice of claim 13, wherein the weighting function is applied to animage in the pair of images by multiplying the intensity of a point inthe image by the value of the weighting function.
 17. The computerreadable program storage device of claim 13, wherein the object ofinterest in each image is the spine.
 18. The computer readable programstorage device of claim 13, wherein the object of interest in each imageis an arm.
 19. The computer readable program storage device of claim 13,wherein the object of interest in each image is a leg.
 20. The computerreadable program storage device of claim 13, wherein the images aremagnetic resonance images.
 21. The computer readable program storagedevice of claim 13, wherein the weighting function is a Gaussian of theformP(x)=exp(−x ²/2σ²) wherein the x-axis is along the image widthdirection, with the image center line defined as x=0, where P(x)=1.0,and σ=width/6.
 22. The computer readable program storage device of claim13, wherein the weighting function is of the formP(x)=σ^(2n)/(σ^(2n) +x ^(2n)), wherein the x-axis is along the imagewidth direction, with the image center line defined as x=0, whereP(x)=1.0, n can be any positive integer and σ=width/6.
 23. The computerreadable program storage device of claim 13, wherein the weightingfunction is of the form${{P(x)} = {{- \left( \frac{2}{w} \right)^{2n}}\left( {x^{2} - \left( \frac{w}{2} \right)^{2}} \right)^{n}}},$wherein the x-axis is along the image width direction, with the imagecenter line defined as x=0, where P(x)=1.0, n is any positive integerand w is the image width.
 24. The computer readable program storagedevice of claim 13, wherein the weighting function is of the formP(x)={−2x/w+1:0≦x≦w/2; 2x/w+1:−w/2≦x<0}, wherein the x-axis is along theimage width direction, with the image center line defined as x=0, whereP(x)=1.0, and w is the image width.
 25. A method for aligning a pair ofdigital images, said method comprising the steps of: providing a pair ofdigital images, wherein each said image comprises a plurality ofintensities corresponding to a domain of points in a D-dimensionalspace, the pair of images present adjacent views of a portion of a sameobject of interest, and each image of the pair of images has an overlapregion; multiplying the intensity of a point in each image of the pairof images by a value of a weighting function, wherein the weightingfunction is a Gaussian of the formP(x)=exp(−x ²/2σ²) wherein the x-axis is along the image widthdirection, with the image center line defined as x=0, where P(x)=1.0,and σ=width/6; aligning the pair of images by correlating the weightedintensities in the overlap region of one image with the weightedintensities in the overlap region of the other image; and joining thetwo images into a composed image at a cutline in the overlap regions ofthe two images.
 26. The method of claim 25, wherein the object ofinterest in each image is the spine.
 27. The method of claim 25, whereinthe object of interest in each image is an arm.
 28. The method of claim25, wherein the object of interest in each image is a leg.
 29. Themethod of claim 25, wherein the images are magnetic resonance images.